Syllabus

Number of Credits: 3 (about 150 hours of study)

Number of Weeks: 16 (Full-time, distance learning)

Objectives:

The aim of the course is to provide a solid foundation in both applied and computational mathematical science with areas of application. The course shifts the emphasis away from rote symbol manipulation, and places more emphasis on numerical methods, with the goal of preparing you to make better use of calculus as a practical problem-solving tool.

Text The textbook covers the course to be studied during one semester. It is intended for students who specialize mainly in Computer Science to help in understanding the fundamental principles of graphical and numerical methods. The book was written using many tools provided by the Open Source Community.

Labs For one session each week you will meet in the Calculus (Engineering) Computer Lab. In your labs, you will work individually on computer assignments and projects, using the computer programming languages (Pascal, C++). Much of your work will be done in the university computer labs, but some of you may choose to do some of your work on your personal computers. As you work, you should discuss your results with your instructor and group mates.

You will do your lab work on PC, which allows you to integrate programs and results of calculations with fully word-processed text to produce a polished report.

Homework Each week, you will be assigned some problems to write up and hand in. These assignments will be graded and returned. In addition, you will be provided with a list of practice problems to do, even though they will not be handed in and graded. Exam problems will sometimes be modeled on these practice problems.

Once a week you will meet with your instructor in a classroom (without computers) to go over problems related to the material covered the previous week.

Contents of the Course

1. Error and Accuracy of Calculations

The errors associated with both calculations and measurements can be characterized with regard to their accuracy and precision. Numerical errors arise from the use of approximations to represent exact mathematical operations and quantities. These include truncation errors, which result when approximations are used to represent exact mathematical procedures, and round-off errors, which result when numbers having limited significant figures are used to represent exact numbers.

2. Numerical Integration

The Newton - Cotes formulas are the most common numerical integration schemes. Closed and open forms of the Newton - Cotes formulas are available. Some of the most widely used formulas are offered in detail: midpoint, trapezoidal and Simpson's rules. Here we have presented the error estimates for each rule. Simple Monte Carlo integration methods are discussed in detail.

3. Solution of Linear Algebraic Equations

Linear sets can be routinely solved by direct and iterative numerical methods: Cramer’s rule, Gaussian elimination with back substitution, Jacobi iteration, Gauss-Siedel iteration. Compute determinant of a matrix. Compute the inverse matrix.

4. Solution Methods of Non-Linear Equations

This chapter is devoted to bracketing methods for finding roots. Graphical methods are used to provide visual insight into the root location. Here we have presented some simple methods: bisection, secant and false position. Newton-Raphson method using derivative is discussed also. Newton-Raphson method is for finding roots of nonlinear systems of equations.

5. Integration of Ordinary Differential Equations

Solution of the Cauchy problem. We shall consider some major types of practical numerical methods for solving ODEs: Euler method, Runge-Kutta methods, and predictor-corrector methods.

5. Interpolation and Extrapolation of Functions

Interpolation and extrapolation schemes must model the function, between or beyond the known points. Polynomial interpolation: Gregory-Newton interpolating polynomial, Newton's interpolatory divided-difference formula, Lagrange interpolating polynomial. Inverse interpolation. Spline functions: linear splines, quadratic splines and cubic splines.

Textbook:

Katsman, J.J. Numerical Methods. Tomsk: TPU Press, 2002.

References:

1. Steven C. Charpa, Raymond P. Canale, Numerical Methods for Engineers: With Programming and Software Applications, 3d ed., McGraw – Hill, New York, 1998.

2. Atkinson, L.V., and P.J. Harley, An Introduction to Numerical Methods with Pascal, Addison-Wesley, Reading, MA, 1983.

3. William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery, Numerical Recipes in C: The Art of Scientific Computing, Cambridge University Press, Cambridge, 1986, 1992.

4. Numerical Methods for Engineers, Bilal M. Ayyub & Richard H. McCuen, Prentice Hall, 1996.

5. An Introduction to Numerical Computations, Second Edition, Sidney Yakowitz & Ferenc Szidarovszky, Macmillan, 1989.

6. FORTRAN 77 for Engineers & Scientists, Fourth Edition, Nyhoff & Leestma, Prentice Hall, 1996.

7. Bird, J.O. Engineering Mathematics, Newnes, 1999.

8. Glyn James and others. Advanced Modern Engineering Mathematics, Addison-Wesley, 1999.

List of Labs:

1. Lab 1. Numerical integration.

2. Lab 2. Solution of linear algebraic equations.

3. Lab 3. Solution methods of non-linear equations.

4. Lab 4. Integration of ordinary differential equations.

5. Lab 5. Interpolation and extrapolation of functions.

RATING

Intermediate assessment #1……………………………………	….50
Intermediate assessment #2……………………………………	….50
Intermediate assessment #3……………………………………	….50
Laboratory work 1………..……………………………………	...100
Laboratory work 2………..……………………………………	...200
Laboratory work 3………..……………………………………	...125
Laboratory work 4………..……………………………………	...100
Laboratory work 5………..……………………………………	...125
Final examination	...200
TOTAL………………………………………………………...	.1000

TIMING

Timing:

Week	Topics	Practice
1	Error and accuracy of calculations
2	Numerical integration: trapezoidal rule, midpoint rule, left and right sum approximation
3	Numerical integration: Simpson's rule, Monte Carlo integration	Lab 1
4	Solution of linear algebraic equations: Gaussian elimination with back substitution
5	Computing the inverse matrix, determinant of a matrix
6	Jacobi iteration, Gauss-Siedel iteration	Lab 2
	Intermediate assessment #1
7	Roots of non-linear equations: bisection method, secant method
8	False position method, Newton - Raphson method
9	Newton - Raphson method for non-linear systems of equations	Lab 3
10	Ordinary differential equations: Euler's method, Runge-Kutta method
11	Multistep methods: the Adams-Bashforth-Moulton schemes	Lab 4
	Intermediate assessment #2
12	Numerical interpolation and extrapolation: polynomial extrapolation
13	Newton's polynomial (divided difference form)
14	Lagrange polynomials, inverse interpolation
15	Interpolation using linear, quadratic & cubic splines	Lab 5
	Intermediate assessment #3
16	Final examination